Predicting pedestrian flow: A methodology and a proof of concept based on real-life data

Building a reliable predictive model of pedestrian motion is challenging: Ideally, such models should be based on observations made in both controlled experiments and in real-world environments. De facto, models are rarely based on real-world observations due to the lack of available data; instead, they are largely based on intuition and, at best, literature values and laboratory experiments. Such an approach is insufficient for reliable simulations of complex real-life scenarios: For instance, an analysis of pedestrian motion under natural conditions at a major German railway station reveals that the values for free-flow velocities and the flow-density relationship differ significantly from widely used literature values. It is thus necessary to calibrate and validate the model against relevant real-life data to make it capable of reproducing and predicting real-life scenarios. In this work the authors aim at constructing such realistic pedestrian stream simulation. Based on the analysis of real-life data, they present a methodology that identifies key parameters and interdependencies that enable them to properly calibrate the model. The success of the approach is demonstrated for a benchmark model, a cellular automaton. They show that the proposed approach significantly improves the reliability of the simulation and hence the potential prediction accuracy. The simulation is validated by comparing the local density evolution of the measured data to that of the simulated data. The authors find that for their model the most sensitive parameters are: the source-target distribution of the pedestrian trajectories, the schedule of pedestrian appearances in the scenario and the mean free-flow velocity. The results emphasize the need for real-life data extraction and analysis to enable predictive simulations.

• Record URL:
  • https://doi.org/10.1371/journal.pone.0083355
• Supplemental Notes:
  • © 2014 Maria Davidich and Gerta Köster
• Authors:
  • Davidich, Maria
  • Köster, Gerta
• Publication Date: 2013

Media Info

• Media Type: Digital/other
• Features: Figures; References;
• Pagination: pp e83355-e83355
• Serial:
  • PLoS One
  • Volume: 8
  • Issue Number: 12
  • Publisher: Public Library of Science
  • EISSN: 1932-6203
  • Serial URL: https://journals.plos.org/plosone/
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Data collection; Free flow speeds; Mathematical models; Pedestrian flow; Pedestrian movement; Simulation
• Geographic Terms: Germany
• Subject Areas: Operations and Traffic Management; Pedestrians and Bicyclists; Planning and Forecasting; I72: Traffic and Transport Planning;

Filing Info

• Accession Number: 01523369
• Record Type: Publication
• Files: TRIS
• Created Date: Apr 25 2014 4:13PM